# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import os
import sys
#import paddlehub as hub
__dir__ = os.path.dirname(os.path.abspath(__file__))

from tqdm import tqdm

from predict_rec import TextRecognizer

from tools.infer.toolfunc import distance, rotate_img_and_point, get_center, area

sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'

import cv2
import numpy as np
import time
import sys

import tools.infer.__utility__.utility as utility
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process

logger = get_logger()


class TextDetector(object):
    def __init__(self, args):
        self.args = args
        self.det_algorithm = args.det_algorithm
        pre_process_list = [{
            'DetResizeForTest': {
                'limit_side_len': args.det_limit_side_len,
                'limit_type': args.det_limit_type
            }
        }, {
            'NormalizeImage': {
                'std': [0.229, 0.224, 0.225],
                'mean': [0.485, 0.456, 0.406],
                'scale': '1./255.',
                'order': 'hwc'
            }
        }, {
            'ToCHWImage': None
        }, {
            'KeepKeys': {
                'keep_keys': ['image', 'shape']
            }
        }]
        postprocess_params = {}
        if self.det_algorithm == "DB":
            postprocess_params['name'] = 'DBPostProcess'
            postprocess_params["thresh"] = args.det_db_thresh
            postprocess_params["box_thresh"] = args.det_db_box_thresh
            postprocess_params["max_candidates"] = 1000
            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
            postprocess_params["use_dilation"] = args.use_dilation
            if hasattr(args, "det_db_score_mode"):
                postprocess_params["score_mode"] = args.det_db_score_mode

        elif self.det_algorithm == "EAST":
            postprocess_params['name'] = 'EASTPostProcess'
            postprocess_params["score_thresh"] = args.det_east_score_thresh
            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
        elif self.det_algorithm == "SAST":
            pre_process_list[0] = {
                'DetResizeForTest': {
                    'resize_long': args.det_limit_side_len
                }
            }
            postprocess_params['name'] = 'SASTPostProcess'
            postprocess_params["score_thresh"] = args.det_sast_score_thresh
            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
            self.det_sast_polygon = args.det_sast_polygon
            if self.det_sast_polygon:
                postprocess_params["sample_pts_num"] = 6
                postprocess_params["expand_scale"] = 1.2
                postprocess_params["shrink_ratio_of_width"] = 0.2
            else:
                postprocess_params["sample_pts_num"] = 2
                postprocess_params["expand_scale"] = 1.0
                postprocess_params["shrink_ratio_of_width"] = 0.3
        else:
            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
            sys.exit(0)

        self.preprocess_op = create_operators(pre_process_list)
        self.postprocess_op = build_post_process(postprocess_params)
        self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
            args, 'det', logger)  # paddle.jit.load(args.det_model_dir)
        # self.predictor.eval()

    def order_points_clockwise(self, pts):
        """
        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
        # sort the points based on their x-coordinates
        """
        xSorted = pts[np.argsort(pts[:, 0]), :]

        # grab the left-most and right-most points from the sorted
        # x-roodinate points
        leftMost = xSorted[:2, :]
        rightMost = xSorted[2:, :]

        # now, sort the left-most coordinates according to their
        # y-coordinates so we can grab the top-left and bottom-left
        # points, respectively
        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
        (tl, bl) = leftMost

        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
        (tr, br) = rightMost

        rect = np.array([tl, tr, br, bl], dtype="float32")
        return rect

    def clip_det_res(self, points, img_height, img_width):
        for pno in range(points.shape[0]):
            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
        return points

    def filter_tag_det_res(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.order_points_clockwise(box)
            box = self.clip_det_res(box, img_height, img_width)
            rect_width = int(np.linalg.norm(box[0] - box[1]))
            rect_height = int(np.linalg.norm(box[0] - box[3]))
            if rect_width <= 3 or rect_height <= 3:
                continue
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.clip_det_res(box, img_height, img_width)
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def __call__(self, img):
        ori_im = img.copy()
        data = {'image': img}
        data = transform(data, self.preprocess_op)
        img, shape_list = data
        if img is None:
            return None, 0
        img = np.expand_dims(img, axis=0)
        shape_list = np.expand_dims(shape_list, axis=0)
        img = img.copy()
        starttime = time.time()

        self.input_tensor.copy_from_cpu(img)
        self.predictor.run()
        outputs = []
        for output_tensor in self.output_tensors:
            output = output_tensor.copy_to_cpu()
            outputs.append(output)

        preds = {}
        if self.det_algorithm == "EAST":
            preds['f_geo'] = outputs[0]
            preds['f_score'] = outputs[1]
        elif self.det_algorithm == 'SAST':
            preds['f_border'] = outputs[0]
            preds['f_score'] = outputs[1]
            preds['f_tco'] = outputs[2]
            preds['f_tvo'] = outputs[3]
        elif self.det_algorithm == 'DB':
            preds['maps'] = outputs[0]
        else:
            raise NotImplementedError
        self.predictor.try_shrink_memory()
        post_result = self.postprocess_op(preds, shape_list)
        dt_boxes = post_result[0]['points']
        if self.det_algorithm == "SAST" and self.det_sast_polygon:
            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
        else:
            dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
        elapse = time.time() - starttime
        return dt_boxes, elapse

def process(img, sorts):
    sp = img.shape
    if distance(sorts[0], sorts[1]) > distance(sorts[1], sorts[2]):
        w = distance(sorts[0], sorts[1])
        h = distance(sorts[1], sorts[2])
        if sorts[1][1] > sorts[0][1]:
            angel = math.acos((sorts[1][0] - sorts[0][0]) / distance(sorts[0], sorts[1]))
            # rotate_img_and_point(img,points,angle,center_x,center_y,resize_rate=1.0):
            src_img, p = rotate_img_and_point(img, get_center(sorts[0], sorts[1], sorts[2], sorts[3]),
                                              (angel / math.pi * 180), sp[1] / 2.0, sp[0] / 2.0)
            # if len(p)>0:
            #  cv2.circle(src_img, (int(p[0][0]), int(p[0][1])), 1, (0, 0, 255), 4)


        else:

            if sorts[1][0] < sorts[0][0]:
                angel = math.acos((sorts[1][0] - sorts[0][0]) / distance(sorts[0], sorts[1]))
                src_img, p = rotate_img_and_point(img, get_center(sorts[0], sorts[1], sorts[2], sorts[3]),
                                                  (-1) * (angel / math.pi * 180), sp[1] / 2.0, sp[0] / 2.0)

                # if len(p) > 0:
                #  cv2.circle(src_img, (int(p[0][0]), int(p[0][1])), 1, (0, 0, 255), 4)


            else:

                angel = math.acos((sorts[1][0] - sorts[0][0]) / distance(sorts[0], sorts[1]))
                src_img, p = rotate_img_and_point(img, get_center(sorts[0], sorts[1], sorts[2], sorts[3]),
                                                  (-1) * (angel / math.pi * 180), sp[1] / 2.0, sp[0] / 2.0)

                # if len(p) > 0:
                #   cv2.circle(src_img, (int(p[0][0]), int(p[0][1])), 1, (0, 0, 255), 4)

    else:
        h = distance(sorts[0], sorts[1])
        w = distance(sorts[1], sorts[2])
        if sorts[1][0] > sorts[2][0]:
            angel = math.acos((sorts[1][0] - sorts[2][0]) / distance(sorts[1], sorts[2]))

            src_img, p = rotate_img_and_point(img, get_center(sorts[0], sorts[1], sorts[2], sorts[3]),
                                              (-1) * (angel / math.pi * 180), sp[1] / 2.0, sp[0] / 2.0)

            # if len(p)>0:
            #  cv2.circle(src_img, (int(p[0][0]), int(p[0][1])), 1, (0, 0, 255), 4)


        else:
            angel = math.acos((sorts[2][0] - sorts[1][0]) / distance(sorts[1], sorts[2]))
            src_img, p = rotate_img_and_point(img, get_center(sorts[0], sorts[1], sorts[2], sorts[3]),
                                              ((angel / math.pi) * 180), sp[1] / 2.0, sp[0] / 2.0)

            # if len(p)>0:
            #  cv2.circle(src_img, (int(p[0][0]), int(p[0][1])), 1, (0, 0, 255), 4)


    #cv2.imshow('1', src_img)
    result_p = []
    res_sp = src_img.shape
    if int((p[0][1] - int(h / 2.0)) * 0.98) < 0:
        result_p.append(0)
    else:
        result_p.append(int((p[0][1] - int(h / 2.0)) * 0.98))

    if int((p[0][1] + int(h / 2.0)) * 1.02) > res_sp[0]:
        result_p.append(res_sp[0])
    else:
        result_p.append(int((p[0][1] + int(h / 2.0)) * 1.02))

    if int((p[0][0] - int(w / 2.0)) * 0.98) < 0:
        result_p.append(0)
    else:
        result_p.append(int((p[0][0] - int(w / 2.0)) * 0.98))

    if int((p[0][0] + int(w / 2.0)) * 1.02) > res_sp[1]:
        result_p.append(res_sp[1])
    else:
        result_p.append(int((p[0][0] + int(w / 2.0)) * 1.02))

    src_img = src_img[result_p[0]:result_p[1], result_p[2]:result_p[3]]
    return src_img



if __name__ == "__main__":
    args = utility.parse_args()
    image_dir = '../../PaddleOCR/tools/infer/images/vin_test'
    #image_dir = '../g_sample_ori'
    #image_file_list = get_image_file_list(args.image_dir)
    text_detector = TextDetector(args)
    text_recognizer = TextRecognizer(args)

    image_file_list = get_image_file_list(image_dir)
    f = open("results.txt", "w",encoding="utf-8")
    length = len(image_file_list)
    count = 0
    total_time = 0
    process_num = 0
    #draw_img_save = "./inference_results"
    #if not os.path.exists(draw_img_save):
    #    os.makedirs(draw_img_save)
    for img_num in tqdm(range(len(image_file_list))):
        image_file = image_file_list[img_num]
        image_name = image_file.split("/")[-1].split(".")[0]
        #image_name = image_file.split("\\")[1].split(".")[0]
        img, flag = check_and_read_gif(image_file)
        if not flag:
            img = cv2.imread(image_file)
        if img is None:
            logger.info("error in loading image:{}".format(image_file))
            continue
        dt_boxes, elapse = text_detector(img)
        if count > 0:
            total_time += elapse
        count += 1
        #logger.info("Predict time of {}: {}".format(image_file, elapse))
        src_im = utility.draw_text_det_res(dt_boxes, image_file)

        sorts = []
        all_sort = []
        if len(dt_boxes) > 0:
            for box in dt_boxes:
                box = np.array(box).astype(np.int32).reshape(-1, 2)
                print(box)
                sorts = []
                for value in box:
                    print("value",value)
                    sorts.append(value.tolist())
                all_sort.append(sorts)
            print("allsort",all_sort)
            max_value = 0
            for num_sort in range(1, len(all_sort)):
                if area(all_sort[num_sort]) > area(all_sort[max_value]):
                    max_value = num_sort



            sorts = all_sort[max_value]
            cut_img = process(img, sorts)


           # cv2.imshow("cut_img", cut_img)
           # cv2.waitKey()

            if cut_img.size > 0:
                imgInfo = cut_img.shape
                height = imgInfo[0]
                width = imgInfo[1]
                deep = imgInfo[2]
                matRotate = cv2.getRotationMatrix2D((width * 0.5, height * 0.5), 180, 1)  # 旋转变化矩阵
                dst = cv2.warpAffine(cut_img, matRotate, (width, height))
                # cv2.imshow("ro_img", dst)

                final_res = {}
                rec_rec, predict_time = text_recognizer([cut_img, dst])
                print(rec_rec)
                final_res['text'], final_res['confidence'] = rec_rec[0]

                _, cf2 = rec_rec[1]

                if final_res['confidence'] < cf2:
                    final_res['text'], final_res['confidence'] = rec_rec[1]

                f.write(image_name + "\t" + final_res['text'] + "\t" + str(final_res['confidence']) + "\n")



            # cv2.imwrite(os.path.join("./temp",os.path.splitext(image_file)))
            # cv2.imshow("cut_img", cut_img)
            else:
                f.write(image_name + "\t" + "" + "\t" + "" + "\n")



        else:
            f.write(image_name + "\t" + "" + "\t" + "" + "\n")

        process_num += 1




        '''
        参数1 必选参数。用于设置旋转中心点，点坐标为OpenCV图像坐标系下的坐标。
        参数2 必选参数。用于设置旋转的角度，单位为度。
        参数3 必选参数。用于设置缩放系数，即对旋转的图像进行缩放。
        '''

        #cv2.waitKey()
        #cv2.destroyAllWindows()

        #print(type(dt_boxes))
        #img_name_pure = os.path.split(image_file)[-1]
        #img_path = os.path.join(draw_img_save,
         #                       "det_res_{}".format(img_name_pure))
        #cv2.imwrite(img_path, src_im)
        #logger.info("The visualized image saved in {}".format(img_path))
    f.close()
   # if count > 1:
    #    logger.info("Avg Time: {}".format(total_time / (count - 1)),"Total Times:",total_time," Counts:", str(count-1))

